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Mid-level (3–6y) and metro hiring amplify competition, but niche agentic/MCP skills limit applicant pool.
Highly specialized agentic AI and MCP tool experience makes cross-industry transferability low, raising sensitivity.
Explicit 3–6 years requirement plus mandatory ML, MCP, LLM orchestration, evals and cloud experience increases strictness.
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Own end-to-end execution of agentic AI roadmap including agent harness and tool design, evaluation pipelines, reliability, and interactive user experiences.
Design and build agentic workflows on top of existing MCP server and ensure production-grade agent behavior integrating frontier LLM models (Gemini, Claude, etc.).
Debug production issues systematically and extend MCP platform with new tools and UI elements for actionable agent output.
3–6 years in ML/AI engineering with hands-on experience building and shipping agentic systems used by real users.
Strong Python skills and expertise in LLM orchestration frameworks or custom harnesses (e.g., LangGraph, Claude Agent SDK).
Experience with building and maintaining evaluation pipelines measuring agent quality, including accuracy, hallucination detection, and regression testing.
Work Experience Required: 3–6 years ML/AI engineering with agentic system production experience.
Experienced in practical use and failure modes of MCP or similar tool-calling frameworks for agent-tool interaction.
Comfortable scoping ambiguous problems and shipping initial versions without fully specified requirements.
Experienced with cloud deployment and familiarity with BigQuery/Spanner data integration into agentic workflows.